AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

1 code implementation • 7 Aug 2023 • Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Richard Powell, Konrad Żołna, Julian Schrittwieser, David Choi, Petko Georgiev, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals

StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution.

Offline RL reinforcement-learning +3

Discovering faster matrix multiplication algorithms with reinforcement learning

2 code implementations • Nature 2022 • Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, Pushmeet Kohli

Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2.

Deep Reinforcement Learning reinforcement-learning +1

Bayesian Optimization in AlphaGo

no code implementations • 17 Dec 2018 • Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas

During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times.

Move Evaluation in Go Using Deep Convolutional Neural Networks

1 code implementation • 20 Dec 2014 • Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.

Game of Go Position